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在matlab软件中,自己编程实现的一个分类算法,算法首先采用kmeans与fcm聚类分析方法进行采样,然后利用svm对选取得样本进行分类-In matlab software, own programming to achieve a classification algorithm, the algorithm first used fcm kmeans cluster analysis methods and sampling, and then use the elections to
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FCM模糊聚类,用于对数据进行分类,能达到较好效果-FCM fuzzy clustering for data classification, can achieve better results
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模糊C-均值算法容易收敛于局部极小点,为了克服该缺点,将遗传算法应用于模糊C-均值算法(FCM)的优化计算中,由遗传算法得到初始聚类中心,再使用标准的模糊C-均值聚类算法得到最终的分类结果。-Fuzzy C- means algorithm is easy to converge to a local minimum point, in order to overcome this drawback, the genetic algorithm is applied to fuzzy C- me
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模糊C-均值算法容易收敛于局部极小点,为了克服该缺点,将遗传算法应用于模糊C-均值算法(FCM)的优化计算中,由遗传算法得到初始聚类中心,再使用标准的模糊C-均值聚类算法得到最优分类结果。-Fuzzy C- means algorithm is easy to converge to a local minimum, in order to overcome this drawback, the genetic algorithm is applied to the fuzzy C- means
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为了精准、稳定地提取滚动轴承故障特征,提出了基于变分模态分解和奇异值分解的特征提取方法,采用标准模糊C均值聚类(fuzzy C means clustering, FCM)进行故障识
别。对同一负荷下的已知故障信号进行变分模态分解,利用
奇异值分解技术进一步提取各模态特征,通过FCM形成标准聚类中心,采用海明贴近度对测试样本进行分类,并通过计算分类系数和“卜均模糊嫡对分类性能进行评价,将该方法
应用于滚动轴承变负荷故障诊断。通过与基于经验模态分解的特征提取方法对比,该方法对标准FCM
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resonance imaging (MRI) data and estimation
of intensity inhomogeneities using fuzzy logic. MRI intensity
inhomogeneities can be attributed to imperfections in the
radio-frequency coils or to problems associated with the acquisition
sequences
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Improved genetic algorithm and fuzzy C- means clustering MATLAB source. The fuzzy C- means algorithm is easy to converge to local minima, in order to overcome the shortcomings of the genetic algorithm is applied to fuzzy C- means algorithm (FCM) opti
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